Automatic video object extraction

ABSTRACT

Automatic video object extraction that defines substantially precise objects is disclosed. In one embodiment, color segmentation and motion segmentation are performed on a source video. The color segmentation segments the video by substantially uniform color regions thereof. The motion segmentation segments the video by moving regions thereof. The color regions and the moving regions are then combined to define the video objects. In varying embodiments, pre-processing and post-processing is performed to further clean the source video and the video objects defined, respectively.

RELATED PATENT APPLICATION(S)

This U.S. Nonprovisional Application for Letters Patent claims priorityfrom, and is a Continuation Application of, U.S. Nonprovisionalapplication for Letters patent No. 09/468,985, filed on Dec. 21, 1999,and entitled “Automatic Video Object Extraction”, now issued as U.S.Pat. No. 6,785,329 on Aug. 31, 2004.

FIELD OF THE INVENTION

This invention relates generally to the extraction of objects fromsource video, and more particularly to such extraction that issubstantially automatic in nature.

BACKGROUND OF THE INVENTION

An increasingly common use of computers and computerized devices is theprocessing of video, such as video captured in real-time, or videocaptured or otherwise input from a storage, such as a hard disk drive, adigital video disc (DVD), a video cassette recorder (VCR) tape, etc. Forthe processing of video, objects within the video usually need to beextracted. Objects can correspond to, for example, semantic objects,which are objects as defined perceptually by the viewer. For example, avideo of a baseball game may have as its objects the various players onthe field, the baseball after it is thrown or hit, etc. Objectextraction is useful for object-based coding techniques, such as MPEG-4,as known within the art; for content-based visual database query andindexing applications, such as MPEG-7, as also known within the art; forthe processing of objects in video sequences; etc.

Prior art object extraction techniques generally fall into one of twocategories: automatic extraction and semi-automatic extraction.Automatic extraction is relatively easy for the end user to perform,since he or she needs to provide little or no input for the objects tobe extracted. Automatic extraction is also useful in real-timeprocessing of video, where user input cannot be feasibly provided inreal time. The primary disadvantage to automatic extraction, however, isthat as performed within the prior art the objects are not definedprecisely. That is, only rough contours of objects are identified. Forexample, parts of the background may be included in the definition of agiven object.

Conversely, semi-automatic object extraction from video requires userinput. Such user input can provide the exact contours of objects, forexample, so that the objects are defined more precisely as compared toprior art automatic object extraction. The disadvantage tosemi-automatic extraction, however, is that user input is in factnecessary. For the lay user, this may be at best inconvenient, and atmost infeasible in the case where the user is not proficient in videoapplications and does not know how to provide the necessary optimalinput. Furthermore, semi-automatic extraction is ill-suited forreal-time processing of video, even where a user is proficient, sincetypically the user cannot identify objects in real time.

Therefore, there is a need to combine the advantages of automatic andsemi-automatic video object extraction techniques. That is, there is aneed to combine the advantageous precise definitions afforded objects bysemi-automatic techniques, with the advantageous ability to perform theobject extraction in real-time, as is allowed with automatic techniques.For these and other reasons, there is a need for the present invention.

SUMMARY OF THE INVENTION

The invention relates to automatic video object extraction. In oneembodiment, color segmentation and motion segmentation are performed ona source video. The color segmentation segments the video bysubstantially uniform color regions thereof. The motion segmentationsegments the video by moving regions thereof. The color regions and themoving regions, referred to as masks in one embodiment of the invention,are then combined to define the video objects.

Embodiments of the invention provide for advantages not found within theprior art. Specifically, at least some embodiments of the inventionprovide for object extraction from video in a substantially automaticmanner, while resulting in objects that are substantially preciselydefined. The motion segmentation mask defines the basic contours of theobjects, while the color segmentation mask provides for more preciseboundaries of these basic contours. Thus, combined, the motion and colorsegmentation masks allow for video object extraction that issubstantially automatic, but which still yields substantially preciselydefined objects.

The invention includes computer-implemented methods, machine-readablemedia, computerized systems, and computers of varying scopes. Otheraspects, embodiments and advantages of the invention, beyond thosedescribed here, will become apparent by reading the detailed descriptionand with reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an operating environment in conjunction withwhich embodiments of the invention can be practiced;

FIG. 2 is a diagram of a frame of a representative source video inconjunction with which embodiments of the invention can be practiced;

FIG. 3 is a diagram of a first object extracted from the representativesource video of FIG. 2;

FIG. 4 is a diagram of a second object extracted from the representativesource video of FIG. 2;

FIG. 5 is a flowchart of a method to perform color segmentationaccording to one embodiment of the invention;

FIG. 6 is a flowchart of a method according to one embodiment of theinvention; and,

FIG. 7 is a diagram of a system according to one embodiment of theinvention.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description of exemplary embodiments of theinvention, reference is made to the accompanying drawings which form apart hereof, and in which is shown by way of illustration specificexemplary embodiments in which the invention may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the invention, and it is to be understood thatother embodiments may be utilized and that logical, mechanical,electrical and other changes may be made without departing from thespirit or scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined only by the appended claims.

Some portions of the detailed descriptions which follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities. Unless specifically stated otherwise asapparent from the following discussions, it is appreciated thatthroughout the present invention, discussions utilizing terms such asprocessing or computing or calculating or determining or displaying orthe like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

Operating Environment

Referring to FIG. 1, a diagram of the hardware and operating environmentin conjunction with which embodiments of the invention may be practicedis shown. The description of FIG. 1 is intended to provide a brief,general description of suitable computer hardware and a suitablecomputing environment in conjunction with which the invention may beimplemented. Although not required, the invention is described in thegeneral context of computer-executable instructions, such as programmodules, being executed by a computer, such as a personal computer.Generally, program modules include routines, programs, objects,components, data structures, etc., that perform particular tasks orimplement particular abstract data types.

Moreover, those skilled in the art will appreciate that the inventionmay be practiced with other computer system configurations, includinghand-held devices, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PC's, minicomputers,mainframe computers, ASICs (Application Specific Integrated Circuits),and the like. The invention may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

The exemplary hardware and operating environment of FIG. 1 forimplementing the invention includes a general purpose computing devicein the form of a computer 20, including a processing unit 21, a systemmemory 22, and a system bus 23 that operatively couples various systemcomponents include the system memory to the processing unit 21. Theremay be only one or there may be more than one processing unit 21, suchthat the processor of computer 20 comprises a single central-processingunit (CPU), or a plurality of processing units, commonly referred to asa parallel processing environment. The computer 20 may be a conventionalcomputer, a distributed computer, or any other type of computer; theinvention is not so limited.

The system bus 23 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memorymay also be referred to as simply the memory, and includes read onlymemory (ROM) 24 and random access memory (RAM) 25. A basic input/outputsystem (BIOS) 26, containing the basic routines that help to transferinformation between elements within the computer 20, such as duringstart-up, is stored in ROM 24. The computer 20 further includes a harddisk drive 27 for reading from and writing to a hard disk, not shown, amagnetic disk drive 28 for reading from or writing to a removablemagnetic disk 29, and an optical disk drive 30 for reading from orwriting to a removable optical disk 31 such as a CD ROM or other opticalmedia.

The hard disk drive 27, magnetic disk drive 28, and optical disk drive30 are connected to the system bus 23 by a hard disk drive interface 32,a magnetic disk drive interface 33, and an optical disk drive interface34, respectively. The drives and their associated computer-readablemedia provide nonvolatile storage of computer-readable instructions,data structures, program modules and other data for the computer 20. Itshould be appreciated by those skilled in the art that any type ofcomputer-readable media which can store data that is accessible by acomputer, such as magnetic cassettes, flash memory cards, digital videodisks, Bernoulli cartridges, random access memories (RAMs), read onlymemories (ROMs), and the like, may be used in the exemplary operatingenvironment.

A number of program modules may be stored on the hard disk, magneticdisk 29, optical disk 31, ROM 24, or RAM 25, including an operatingsystem 35, one or more application programs 36, other program modules37, and program data 38. A user may enter commands and information intothe personal computer 20 through input devices such as a keyboard 40 andpointing device 42. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, video camera,or the like. These and other input devices are often connected to theprocessing unit 21 through a serial port interface 46 that is coupled tothe system bus, but may be connected by other interfaces, such as aparallel port, game port, an IEEE 1394 port (also known as FireWire), ora universal serial bus (USB). A monitor 47 or other type of displaydevice is also connected to the system bus 23 via an interface, such asa video adapter 48. In addition to the monitor, computers typicallyinclude other peripheral output devices (not shown), such as speakersand printers.

The computer 20 may operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer 49.These logical connections are achieved by a communication device coupledto or a part of the computer 20, the invention is not limited to aparticular type of communications device. The remote computer 49 may beanother computer, a server, a router, a network PC, a client, a peerdevice or other common network node, and typically includes many or allof the elements described above relative to the computer 20, althoughonly a memory storage device 50 has been illustrated in FIG. 1. Thelogical connections depicted in FIG. 1 include a local-area network(LAN) 51 and a wide-area network (WAN) 52. Such networking environmentsare commonplace in office networks, enterprise-wide computer networks,intranets and the Internet, which are all types of networks.

When used in a LAN-networking environment, the computer 20 is connectedto the local network 51 through a network interface or adapter 53, whichis one type of communications device. When used in a WAN-networkingenvironment, the computer 20 typically includes a modem 54, a type ofcommunications device, or any other type of communications device forestablishing communications over the wide area network 52, such as theInternet. The modem 54, which may be internal or external, is connectedto the system bus 23 via the serial port interface 46. In a networkedenvironment, program modules depicted relative to the personal computer20, or portions thereof, may be stored in the remote memory storagedevice. It is appreciated that the network connections shown areexemplary and other means of and communications devices for establishinga communications link between the computers may be used.

Overview

In this section of the detailed description, an overview of objectextraction, such as can be performed by embodiments of the invention, isprovided. The objects extracted in at least some embodiments of theinvention are semantic objects. Semantic objects represent meaningfulentities in a source video, from a perceptual standpoint of users.

For example, in the diagram of FIG. 2, within the source video 200 areshown two objects, a person 200, and a bird 204. Applying video objectextraction in accordance with embodiments of the invention yieldsseparation of the person 200 and the bird 204 from the source video 200in such a manner that the boundaries of the objects are defined at leastsubstantially precisely, against any other objects within the video 200and the background of the video 200.

Thus, applying embodiments of the invention to perform object extractionon the source video 200 of FIG. 2 yields the person 202 by itself, asshown in the frame 300 of the diagram of FIG. 3, and the bird 204 byitself, as shown in the frame 400 of the diagram of FIG. 4. Those ofordinary skill within the art can appreciate, however, that the overviewpresented in this section in conjunction with FIGS. 2-4 is for exampleand illustration purposes only, and that the invention itself is notlimited to the example provided herein.

Color Segmentation

In this section of the detailed description, color segmentation asperformed in accordance with an embodiment of the invention isdescribed. The color segmentation in one embodiment is performed on aframe of a source video, to segment the video by substantially uniformcolor regions thereof. That is, a color segmentation mask is generatedthat defines substantially precise boundaries of the objects extractedfrom the source video. Those of ordinary skill within the art canappreciate that many techniques exist within the prior art to performcolor segmentation, such as edge detection, region techniques, Maximum Aposteriori Probability (MAP) techniques, etc., and that the inventionitself is not limited to a particular technique. The embodiment of theinvention described herein utilizes a technique that extends and isbased on the technique described in the reference Chuang Gu andMing-Chieh Lee, Tracking of Multiple Semantic Video Objects for InternetApplications, SPIE, Visual Communications and Image Processing 1999,volume 3653, as known within the art.

Referring to FIG. 5, a flowchart of a method to perform colorsegmentation according to an embodiment of the invention, extending andbased on the reference identified in the previous paragraph, is shown.From the start in 500, the method proceeds to 502, where the methoddetermines a seed pixel of a frame of a source video. Initially, a seedcan be chosen to be the upper-left corner pixel in a rectangular frame.After that, a seed is randomly chosen from the region(s) have not beenincluded in any segmented regions. If no seed pixels are left, then themethod proceeds to 514. Otherwise the method puts the seed into a seedbuffer in 504. A seed pixel is then obtained from the buffer in 506,unless there are no seeds left in the buffer, in which case the methodproceeds back to 502.

In 508, the seed pixel is grown by a neighbor pixel or a neighborhood ofpixels. The seed pixel is grown by a neighborhood of pixels surroundingthe pixel as governed by the constraint that a substantially homogenouscolor region is to be generated. In one embodiment, the homogeneity of aregion is controlled by the difference of the maximum and minimum valueswithin a region. For example, for a color image, the value of a pixel inone embodiment is a vector including the red, green and blue colorchannels in the form {r, g, b}. Thus, the maximum and minimum values ofa region are {max {r}, max{g}, max{b}}, and {min{r}, min{g}, min{b}},respectively. If the difference of the maximum and minimum values of aregion does not exceed a predetermined threshold, then it is deemed auniform region.

Therefore, in 510, after the region has been grown in 508, it isdetermined whether the region is still of substantially uniform color.If not, then the method proceeds back to 506, to obtain a new seed fromthe buffer, and to start the process of obtaining substantially uniformcolor regions over again. Otherwise, the neighboring pixel or pixels ofthe seed pixel is placed into the seed buffer in 512, to continuegrowing the same substantially uniform color region by also proceedingback to 506. The same substantially uniform color region is continued tobe grown by virtue of the fact that the neighboring pixel or pixels ofthe seed pixel have been placed into the seed buffer in 512.

Once all the substantially uniform color regions have been determined,and there are no seeds left in the buffer, nor can further seeds befound within the frame of the source video, then the method proceedsfrom 502 to 514. In 514, smaller substantially uniform color regions aremerged into larger substantially uniform color regions. This mergingremoves relatively smaller regions by integrating them into relativelylarger regions. In one embodiment, this is accomplished by merging allthe regions with the number of pixels less than a certain threshold (<10pixels) to its neighbor regions. The method then ends in 516.

As has been noted, color segmentation can be performed on one or moreframes of the source video. The invention is not limited, however, bythe manner which is followed to select the frame that will ultimately beused as the color segmentation mask. In one embodiment, the frame isselected by the user, either before or after color segmentation has beenperformed (i.e., the user selects a frame on which color segmentation isperformed, or color segmentation is performed on a number of frames, andthe user selects one of these frames). In another embodiment, the frameis predetermined. For example, for off-line processing of capturedvideo, the first frame may be always selected. As another example, forreal-time processing of video, the last frame may always be selected.

Motion Segmentation and Combination of Multiple Frames

In this section of the detailed description, motion segmentation asperformed in accordance with an embodiment of the invention isdescribed, as well as the combination of multiple frames for such motionsegmentation to generate a cleaner motion segmentation mask. The motionsegmentation in one embodiment is performed on a plurality of frames ofa source video, such as two frames, as will be described, although theinvention itself is not so limited. Those of ordinary skill within theart can appreciate that many techniques exist within the prior art toperform motion segmentation, such as optical flow techniques,block-based matching techniques, Maximum A posteriori Probability (MAP)techniques, simultaneous motion estimation and segmentation techniques,etc., and that the invention itself is not limited to a particulartechnique. The motion segmentation segments a source video by movingregions thereof, and defines approximate boundaries of objects extractedfrom the source video.

In one embodiment, motion segmentation is obtained as follows. First, amotion vector is first obtained by region matching. For each uniformregion generated from the color segmentation described in the precedingparagraph, prior to combining the smaller regions with the largerregions, a motion vector is obtained by determining the best match in anext frame of the source video. This is particularly described in thereference noted in the preceding section of the detailed description.This region-based motion estimation technique substantially ensures thateach color-segmented region has the same motion vector.

In one embodiment, the matching window is set to a sixteen-by-sixteenpixel window, and the matching criterion is to determine the leastmatching error of the region. The matching error is defined as:

${{ERROR}\left( {n,i} \right)} = {\sum\limits_{p \in R_{n}}{{{I_{t}(p)} - {I_{t + 1}\left( {p + V_{({n,i})}} \right)}}}}$ERROR(n, i) is the matching error for region n with motion vector V_(n,i). I_t and I_t+1 represent the current and next frame, respectively.R_n denotes region n. Operator ||*|| denotes the sum of absolutedifference between two vectors. Finally, V_(n, i)<=V_max, where V_max isthe searching range. The motion vector of region n is defined as:

${V(n)} = {\underset{V_{({n,i})}}{\arg\;\min}\mspace{14mu}{{ERROR}\left( {n,i} \right)}}$

After obtaining a motion vector for each region, a motion mask can thenbe obtained:

V(i, j) = V(n), (i, j) ∈ Region  nV(i, j) = V_(x)(i, j) + V_(y)(i, j)${M\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{11mu}{{V\left( {i,j} \right)}}} \geq {T1}} \\0 & {otherwise}\end{matrix} \right.$V(i, j) is the motion vector of pixel (i, j), and V_x(i,j) and V_y(i,j)are the projection of V(i, j) on the x and y coordinates, respectively.M(i, j) represents each pixel in the motion mask, where T1 is thepredetermined threshold set according to the motion. In one embodiment,T1 is set to one for slow motion, and to anywhere from two to three forfast motion.

In one embodiment, the frame interval is adapted according to theestimated motion. Most motion estimation is based on two continuousframes. However, this is not always valid in some sequences, where themotion of moving objects is minimal, and leads to a sparse motion field.Therefore, the interval of two frames is adapted. For a fast motionfield, the interval can be set small, while for slow motion, theinterval can be set large.

In one embodiment, multiple frames are combined to decrease errorsassociated with the assignment of regions incorrectly assigned to amoving object due to noise. In other words, the incorrect motion causedby random noise is removed by checking multiple motion masks to generatethe final motion mask, since random noise will not be constant acrossmultiple frames. Furthermore, the uncovered background can bedistinguished from true moving regions.

This combination of multiple frames is accomplished in one embodiment asfollows. First, the frequency of a pixel is assigned to a moving objectin a number of motion masks, such as ten or more. If the frequency ishigher than a predetermined threshold, then this pixel is determined asa moving pixel. Otherwise, it is designated as background and is removedfrom the final motion mask. In one embodiment, the threshold is fiftypercent. Thus,

${C\left( {i,j} \right)} = {\sum\limits_{s = 1}^{S}{M_{s}\left( {i,j} \right)}}$${M\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu}{{C\left( {i,j} \right)}/S}} > {T2}} \\0 & {otherwise}\end{matrix} \right.$M_s(i,j) represents each motion mask being combined, S denotes thenumber of total motion masks used in combination, C(i,j) denotes thetotal number of times pixel (i,j) is assigned as a moving pixel in Smotion masks, and T2 is the threshold.Methods

In this section of the detailed description, methods according tovarying embodiments of the invention are described. The description ismade with reference to FIG. 6, which is a flowchart of acomputer-implemented method according to one embodiment of theinvention. The computer-implemented method is desirably realized atleast in part as one or more programs running on a computer—that is, asa program executed from a computer-readable medium such as a memory by aprocessor of a computer. The programs are desirably storable on amachine-readable medium such as a floppy disk or a CD-ROM, fordistribution and installation and execution on another computer.

Referring to FIG. 6, from the start of the method in 600, the methodproceeds to 602, in which in one embodiment pre-processing of a sourcevideo from which objects are to be extracted is accomplished.Pre-processing is performed in one embodiment to remove noise from thesource video prior to performing color segmentation. In one embodiment,a median filter is used on each of the red, green and blue colorchannels of the source video, or one or more frames thereof.Pre-processing is not necessary for the invention, however.

Next, in 604, color segmentation is performed. Color segmentation can beaccomplished in one embodiment as described in a preceding section ofthe detailed description, although the invention is not so limited. Theresulting mask of the color segmentation is then optimized in oneembodiment by merging smaller regions into larger regions in 606, andsuch that a certain frame of the color segmented source video isselected in 608, as has also been described in a preceding section ofthe detailed description. The final color segmentation mask is then usedas input in 616.

In the embodiment of FIG. 6, the color segmentation mask resulting from604 is used as the basis for motion segmentation in 610, to generate amotion segmentation mask, as described in the preceding section of thedetailed description. However, the invention does not require the motionsegmentation mask to be generated from the color segmentation mask. Inone embodiment, the mask is optimized by combining multiple-frame masksin 612, as also described in the preceding section of the detaileddescription. However, the invention is not limited to motionsegmentation as described in the preceding section. The final motionsegmentation mask is then used as input in 616.

In 614, a third mask used as input in 616 is generated, which isreferred to as a frame difference mask. In one embodiment, the framedifference mask is generated from two successive frames of the sourcevideo, and is generated to provide for correction of errors that mayresult from the motion segmentation mask. The difference mask isobtained in one embodiment as follows:

D(i, j) = I_(t)(i, j) − I_(t + m)(i, j)${{DM}\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu}{D\left( {i,j} \right)}} > {T4}} \\0 & {otherwise}\end{matrix} \right.$The difference mask is DM(i, j), where I_x represents frame x, and T4 isa predetermined threshold, such as ten to twenty pixels.

In 616, the resulting color segmentation, motion segmentation, and framedifference masks are combined to define the objects of the source videoin an automatic and substantially precise manner. In one embodiment, themedian result of color segmentation and motion segmentation is firstdetermined. The frame difference mask is then combined with this medianresult to generate the final mask which defines the objects of thesource video.

Thus, while color segmentation identifies the exact edges of objects, ittypically results in over segmentation. Furthermore, while motionsegmentation generates a coarse mask of moving objects, the boundariesidentified are too rough to provide for exact object extraction.Therefore, the color segmentation and motion segmentation masks arecombined, to extract moving objects with substantially pixel-wiseaccuracy.

In one embodiment, this initial combination of motion and colorsegmentation masks is accomplished by a mapping operation. For eachregion generated by color segmentation, the corresponding region isfound in the motion segmentation mask. If the percent of the region thatis assigned to a moving object exceeds a predetermined threshold, thenthe whole region is deemed to be part of the moving object. In oneembodiment, this threshold is between fifty and sixty percent.

Thus,

${B(N)} = {\sum\limits_{{({i,j})} \in \; N}{M\left( {i,j} \right)}}$${J(N)} = \left\{ {{\begin{matrix}1 & {{{if}\mspace{14mu}{{B(N)}/{A(N)}}} > {T3}} \\0 & {otherwise}\end{matrix}F\;{M\left( {i,j} \right)}} = {{{J(N)}\mspace{31mu}{if}\mspace{14mu}\left( {i,j} \right)} \in N}} \right.$N represents the color segmented region, A(N) is the area of region N,M(i, j) is the pixel in the combined motion mask, and FM(I,j) is thepixel in the final mapped mask. T3 is the threshold.

The combined motion and color segmentation mask is then combined withthe frame difference mask, as follows:

${B(N)} = {\sum\limits_{{({i,j})} \in \; N}{{DM}\left( {i,j} \right)}}$${J(N)} = \left\{ {{\begin{matrix}1 & {{{if}\mspace{14mu}{{B(N)}/{A(N)}}} > {T3}} \\0 & {otherwise}\end{matrix}{{FD}\left( {i,j} \right)}} = {{{{J(N)}\mspace{20mu}{if}\mspace{14mu}\left( {i,j} \right)} \in {N{F\left( {i,j} \right)}}} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} F\;{M\left( {i,j} \right)}} = {{1\mspace{14mu}{and}\mspace{14mu}{{FD}\left( {i,j} \right)}} = 1}} \\0 & {otherwise}\end{matrix} \right.}} \right.$F(i, j) is the final mask in which the color segmentation, motionsegmentation, and frame-difference masks have been combined. FD(i, j) isthe frame-difference mask such that the mask has been mapped in the samemanner as FM(i, j) has been mapped, so that the two can be compared togenerate F(i, j).

As can be appreciated by those of ordinary skill within the art, theframe-difference mask, while providing for more precise automaticextraction of video objects, is not required in one embodiment of theinvention. Thus, in one embodiment, F(i, j) can be obtained simply bysetting it equal to FM(i, j), which is the interim result of combiningthe color and the motion segmentation masks. That is, combining themoving regions resulting from the motion segmentation and thesubstantially uniform color regions resulting from the colorsegmentation can be used in one embodiment to define the objects of thesource video.

Furthermore, other masks can be introduced into 616 in addition toand/or in lieu of any of the color segmentation, motion segmentation,and frame difference masks. Such masks can include a depth mask,providing for information relating to the depth of the objects of thesource video, a texture mask, providing for information relating to thetexture of the objects of the source video, etc. In such embodiments,all of the masks to be used are combined to generate the final mask thatidentifies the objects extracted from the source video.

Next, in one embodiment, post-processing of the mask generated in 616 isperformed in 618, for example, to remove noise from the mask.Post-processing can include in one embodiment removing small holes fromthe objects identified to generate integrated objects, and can inanother embodiment also include removing small regions from backgroundareas of the source video to generate cleaner objects. In oneembodiment, this is accomplished by first merging smaller regions withlarger regions, as described in a preceding section of the detaileddescription in the context of color segmentation. Then, a morphologicaloperator(s), such as open and/or close, as known within the art, isapplied. However, as can be appreciated by those of ordinary skillwithin the art, post-processing is not required by the invention itself.The method of FIG. 6 finally ends at 620.

Systems and Computers

In this section of the detailed description, systems and computersaccording to varying embodiments of the invention are described. Thedescription is made with reference to FIG. 7, which is a diagram of acomputer 699 according to an embodiment of the invention. The computer699 can, for example, correspond to the computer described inconjunction with FIG. 1 in a preceding section of the detaileddescription. The computer 699 includes a processor 700, acomputer-readable medium 702, and a source 704 from which the sourcevideo from which objects are to be extracted is obtained. The medium 702can include non-volatile memory, storage devices such as hard diskdrives, as well as volatile memory such as types of random-access memory(RAM).

The medium 702 stores data representing at least one frame 706 of thesource video, which itself has a number of frames, and is obtained fromthe source 704, such as a video camera, a video cassette recorder (VCR)tape, a digital video disc (DVD), etc. The medium 702 also stores datarepresenting the objects 708 extracted from the source video. The medium702 can in one embodiment correspond to a means for storing datarepresenting the frame(s) 706 and the objects 708. The objects areextracted by a computer program 710, which is executed by the processor700 from the medium 702.

The program 710 is thus designed to extract the objects 708 from thevideo by generating and then combining a number of masks from the video,in one embodiment, as has been described in preceding sections of thedetailed description. In one embodiment, the program 710 can correspondto a means for generating and combining the masks. These masks caninclude a color segmentation mask, a motion segmentation mask, a framedifference mask, a texture mask, and/or a depth mask, as has beendescribed in the preceding section of the detailed description. Thecolor mask can be used to define substantially precise boundaries of theobjects, the motion segmentation mask to define approximate boundaries,and the frame difference mask to correct errors within the motionsegmentation mask.

Furthermore, the program 710 can either pre-process one or more framesof the source video to remove noise, post-process the objects extractedin the finally generated mask to remove noise, or both, although theinvention is not so limited. Pre-processing and post-processing can beperformed as has been described in the preceding section of the detaileddescription. In one embodiment, the program 710 also corresponds to themeans for pre-processing and/or post-processing.

CONCLUSION

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement which is calculated to achieve the same purpose maybe substituted for the specific embodiments shown. This application isintended to cover any adaptations or variations of the presentinvention. Therefore, it is manifestly intended that this invention belimited only by the following claims and equivalents thereof.

1. A computer implemented method for extracting objects from a sourcevideo comprising: performing motion segmentation on a plurality offrames of the source video to segment the source video by moving regionsthereof to define basic contours of the objects of the source video;performing color segmentation on a frame of the source video to segmentthe source video by substantially uniform color regions thereof todefine more precise boundaries of the objects of the source video asmeasured against the basic contours of the objects of the source videoas defined by the motion segmentation; and, combining at least themoving regions resulting from the motion segmentation and thesubstantially uniform color regions resulting from the colorsegmentation to define still more precisely the objects of the sourcevideo by, at least partially, deeming a substantially uniform colorregion to be part of a moving object if a percent of the substantiallyuniform color region that is assignable to one or more moving regionsexceeds a predetermined threshold.
 2. The method of claim 1, wherein thepredetermined threshold is between fifty and sixty percent.
 3. Acomputer-readable medium having computer instructions stored thereon forexecution by a processor to perform a method comprising: generating aplurality of masks from a source video having a plurality of frames, theplurality of masks including at least a color segmentation mask, amotion segmentation mask, and a frame difference mask; wherein the framedifference mask is generated from at least two frames of the pluralityof frames without using another mask; and, combining the plurality ofmasks to define a plurality of objects of the source video; whereingenerating the plurality of masks comprises generating at least thecolor segmentation mask to define substantially precise boundaries ofthe objects, the motion segmentation mask to define approximateboundaries of the objects, and the frame difference mask to correcterrors within the motion segmentation mask; and wherein the plurality ofobjects of the source video resulting from the combining are moreprecise than the substantially precise boundaries of the objects definedby the color segmentation mask, which are more precise than theapproximate boundaries of the objects defined by the motion segmentationmask.
 4. The medium of claim 3, wherein generating the colorsegmentation mask comprises growing substantially uniform color regionsof a frame of the plurality of frames of the source video.
 5. The mediumof claim 3, wherein generating the motion segmentation mask comprisesgenerating the motion segmentation mask from the plurality of frames ofthe source video.
 6. The medium of claim 3, wherein the method furthercomprises pre processing a frame of the plurality of frames of thesource video to remove noise prior to generating the plurality of masks.7. The medium of claim 3, wherein the method further comprises postprocessing the plurality of objects of the source video to remove noisefrom the objects.
 8. A computer comprising: a processor; at least onecomputer readable medium to store data representing: at least two framesof a plurality of frames of a source video, and, a plurality of objectsextracted from the source video; and, a computer program executed by theprocessor from the at least one computer readable medium and designed toextract the plurality of objects from the source video by generating aplurality of masks from the source video and then combining theplurality of masks by, at least partially, deeming a substantiallyuniform color region to be part of a moving object if a percent of thesubstantially uniform color region that is assignable to one or moremoving regions exceeds a predetermined threshold, the plurality of masksincluding: a motion segmentation mask that segments the video bysubstantially uniform color regions to define basic contours of theplurality of objects of the source video based on moving regionsthereof, a color segmentation mask that segments the video by movingregions to define more precise boundaries, as compared to the basiccontours, of the plurality of objects of the source video based onsubstantially uniform color regions thereof, and a frame differencemask.
 9. The computer of claim 8, wherein the plurality of objectsextracted from the source video are better defined than the preciseboundaries of the plurality of objects as defined by the colorsegmentation mask, and the precise boundaries of the plurality ofobjects are more defined than the basic contours of the plurality ofobjects as, defined by the motion segmentation mask.
 10. The computer ofclaim 8, wherein the program is further designed to pre process the atleast two frames of the plurality of frames of the source video toremove noise.
 11. The computer of claim 8, wherein the program isfurther designed to post process the plurality of objects extracted fromthe source video to remove noise.
 12. At least one computer-readablemedium having computer instructions stored thereon for execution by aprocessor to transform a general purpose computer to a special purposecomputer comprising: means for storing data representing: at least twoframes of a plurality of frames of a source video, and, a plurality ofobjects extracted from the source video; and, means for: (a) generatinga plurality of masks from the source video, the plurality of masksincluding: a color segmentation mask segmenting the source video bysubstantially uniform color regions to define substantially preciseboundaries of the plurality of objects, a motion segmentation masksegmenting the source video by moving regions thereof to defineapproximate boundaries of the plurality of objects, and a framedifference mask to reflect differences between the at least two framesof the source video, and, (b) combining the plurality of masks toextract the plurality of objects from the source video by, at leastpartially, deeming a substantially uniform color region to be part of amoving object if a percent of the substantially uniform color regionthat is assignable to one or more moving regions exceeds a predeterminedthreshold.
 13. The at least one computer-readable medium of claim 12,wherein the substantially precise boundaries of the plurality of objectsdefined by the color segmentation mask are more precise than theapproximate boundaries of the plurality of objects defined by the motionsegmentation mask, and the plurality of objects extracted from thesource video are more precise than the precise boundaries of theplurality of objects defined by the color segmentation mask.
 14. The atleast one computer-readable medium of claim 12, wherein the means isfurther for pre processing the at least two frames of the plurality offrames of the source video to remove noise.
 15. The at least onecomputer-readable medium of claim 12, wherein the means is further forpost processing the plurality of objects extracted from the source videoto remove noise.
 16. At least one computer-readable medium havingcomputer instructions stored thereon for execution by a processor toperform a method comprising: performing motion segmentation on at leastthree frames of a plurality of frames of video to segment the video bymoving regions and to thereby define basic contours of objects of thevideo; performing color segmentation on at least one frame of theplurality of frames of the video to segment the video by substantiallyuniform color regions and to thereby define more precise boundaries ofthe objects of the video as compared to the basic contours of theobjects of the video as defined by the motion segmentation; and,combining the substantially uniform color regions resulting from thecolor segmentation and the moving regions resulting from the motionsegmentation to further define the objects of the video by, at leastpartially, deeming a substantially uniform color region to be part of amoving object if a percent of the substantially uniform color regionthat is assignable to one or more moving regions exceeds a predeterminedthreshold.
 17. The at least one computer-readable medium of claim 16,wherein the performing motion segmentation comprises determining acombination motion mask based on a plurality of individual motion masksand responsive to at least one threshold.
 18. At least onecomputer-readable medium having computer instructions stored thereon forexecution by a processor to perform a method comprising: generating acolor segmentation mask that segments the video by substantially uniformcolor regions to define contours of objects of a video to a firstprecision; generating a motion segmentation mask that segments the videoby moving regions to define the contours of the objects of the video toa second precision; generating a frame difference mask that reflectsdifferences in the video between a first frame and a second frame of thevideo on a per pixel basis responsive to a predetermined threshold; andcombining the color segmentation mask, the motion segmentation mask, andthe frame difference mask to define the objects of the video by, atleast partially, deeming a substantially uniform color region to be partof a moving object if a percent of the substantially uniform colorregion that is assignable to one or more moving regions exceeds apredetermined threshold.
 19. The at least one computer-readable mediumof claim 18, wherein the objects of the video defined by the combiningof the color segmentation mask, the motion segmentation mask, and theframe difference mask are yet more precise than the basic contours ofthe objects of the video defined by the moving regions and the moreprecise boundaries of the objects of the video defined by thesubstantially uniform color regions.
 20. The at least onecomputer-readable medium of claim 18, wherein the combining of the colorsegmentation mask, the motion segmentation mask, and the framedifference mask to define the objects of the video comprises: combiningthe color segmentation mask and the motion segmentation mask to generatea first intermediate mask; combining the color segmentation mask and theframe difference mask to generate a second intermediate mask; andcombining the first intermediate mask and the second intermediate maskto produce a final mask.